Were AI strategies identified at go or starcraft games and how?
Truth be told, I have no idea how to play Go, but luckily this is a AI forum and not a Go forum. Addressing your questions about the specific strategies that AI discovered, there's a paper released by OpenAI titled &quot;Mastering the game of Go without human knowledge&quot; (https://deepmind.com/research/publications/mastering-game-go-without-human-knowledge). Here, there's a section titled &quot;Knowledge learned by AlphaGo Zero.&quot; In it, they mention that AlphaGo Zero discovered some variants of common corner sequences. I think it may be worth checking out. The section also lists some other common Go strategies that AlphaGo Zero learned. I believe during the training process, AlphaGo Zero was trained using self-play, where the AI played itself over and over again to get better.
To address your questions about chess. I don't think there's anything limiting AI from learning strategy in chess or anything about chess that limits AI from learning an effective strategy. Check out this paper for more: https://deepmind.com/research/publications/Mastering-Atari-Go-Chess-and-Shogi-by-Planning-with-a-Learned-Model
Here the authors present a general reinforcement learning model capable of mastering multiple common board games. I'm not too familiar with strategies in Star Craft, however, here's a link containing some other projects where strategies that emerge by the AI are shown: https://openai.com/blog/competitive-self-play/